An Efficient Algorithm for Extracting Railway Tracks Based on Spatial-Channel Graph Convolutional Network and Deep Neural Residual Network
The accurate detection of railway tracks is essential for ensuring the safe operation of railways. This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature extraction from high-resolution aerial imagery. The t...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-08-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/13/9/309 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The accurate detection of railway tracks is essential for ensuring the safe operation of railways. This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature extraction from high-resolution aerial imagery. The traditional encoder–decoder architecture is expanded with GCN, which improves neighborhood definitions and enables long-range information exchange in a single layer. As a result, complex track features and contextual information are captured more effectively. The deep neural residual network, which incorporates depthwise separable convolution and an inverted bottleneck design, improves the representation of long-distance positional information and addresses occlusion caused by train carriages. The scSE attention mechanism reduces noise and optimizes feature representation. The algorithm was trained and tested on custom and Massachusetts datasets, demonstrating an 89.79% recall rate. This is a 3.17% improvement over the original U-Net model, indicating excellent performance in railway track segmentation. These findings suggest that the proposed algorithm not only excels in railway track segmentation but also offers significant competitive advantages in performance. |
|---|---|
| ISSN: | 2220-9964 |